On a class of non-stationary, compactly supported spatial covariance functions
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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http://dx.doi.org/10.1007/s00477-011-0510-8 |
Metadatos
Título
On a class of non-stationary, compactly supported spatial covariance functionsFecha de publicación
2013-02Editor
Springer-VerlagISSN
1436-3240Cita bibliográfica
Stochastic Environmental Research and Risk Assessment, 27, 2, p. 297-309Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://link.springer.com/article/10.1007/s00477-011-0510-8Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Globally supported covariance functions are generally associated with dense covariance matrices, meaning severe numerical problems in solution feasibility. These problems can be alleviated by considering methods ... [+]
Globally supported covariance functions are generally associated with dense covariance matrices, meaning severe numerical problems in solution feasibility. These problems can be alleviated by considering methods yielding sparse covariance matrices. Indeed, having many zero entries in the covariance matrix can both greatly reduce computer storage requirements and the number of floating point operations needed in computation. Compactly supported covariance functions considerably reduce the computational burden of kriging, and allow the use of computationally efficient sparse matrix techniques, thus becoming a core aspect in spatial prediction when dealing with massive data sets. However, most of the work done in the context of compactly supported covariance functions has been carried out in the stationary context. This assumption is not generally met in practical and real problems, and there has been a growing recognition of the need for non-stationary spatial covariance functions in a variety of disciplines. In this paper we present a new class of non-stationary, compactly supported spatial covariance functions, which adapts a class of convolution-based flexible models to non-stationary situations. Some particular examples, computational issues, and connections with existing models are considered. [-]
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